Browsing by Subject "Robust optimization"
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Item Impact of budget uncertainty on network-level pavement condition : a robust optimization approach(2013-12) Al-Amin, Md; Zhang, Zhanmin, 1962-Highway agencies usually face budget uncertainty for pavement maintenance and rehabilitation activities due to limitation in resources and changes in government policies. Highway agencies perform maintenance planning for the pavement network commonly based on the nominal available budget without taking the variability of budget into consideration. The maintenance program based on deterministic budget consideration results in suboptimal maintenance decisions that impact the overall network conditions, if the budget falls short in some future year in the planning horizon. As a result, it is important for highway agencies to adopt maintenance and rehabilitation policies that are protected against the uncertainty in maintenance and rehabilitation budget. In this study a multi-period linear integer programming model is proposed with its robust counterpart considering uncertain maintenance and rehabilitation budget. The proposed model is able to provide a maintenance and rehabilitation program for the pavement network that results in minimal impact of budget variability on the network conditions. A case study was carried out for a network of ten pavement sections. The solution of the robust optimization model was compared to those with deterministic model. The results show that the robust optimization model is an attractive method that can minimize the effect of budget uncertainty on pavement conditions at the network level.Item Optimizing cross-dock operations under uncertainty(2011-12) Sathasivan, Kanthimathi; Waller, S. Travis; Bhat, Chandra R.; Machemehl, Randy B.; Morton, David P.; Zhang, ZhanminCross-docking is an important transportation logistics strategy in supply chain management which reduces transportation costs, inventory holding costs, order-picking costs and response time. Careful planning is needed for successful cross-dock operations. Uncertainty in cross-dock problems is inevitable and needs to be addressed. Almost all research in the cross-dock area assumes determinism. This dissertation considers uncertainty in cross-dock problems and optimizes these problems under uncertainty. We consider uncertainty in processing times, using scenario-based and protection-based robust approaches. Using a heuristic method, we find a lower and upper bound and combine that with a meta-heuristic method to solve the problem. Also, we consider problems in two different industries (Goodwill and H-E-B) and address the uncertainties that happen frequently in their operations. The scenario-based robust optimization model for the unloading problem using a min max objective is presented with examples. A surrogate heuristic procedure is used to find a robust solution. Next, a two-space genetic algorithm, a meta-heuristic procedure, is applied to the unloading problem using the bounds obtained by the heuristic procedure. The results are closer to the optimal solution than those obtained by the two-space genetic algorithm without bounds. When compared with the regular genetic algorithm with bounds, the two-space algorithm performs well. The protection-based approach considers a limit on the number of coefficients allowed to change with data uncertainty, protecting against the degree of conservatism. The management of trucks and reduction of overtime pay in the cross-dock operations of Goodwill is addressed through two models and uncertainty is applied to those models. A combined cross-dock operations model together with demand is formulated and the uncertainties are discussed for H-E-B operations. This dissertation does not address the recycling operation within the cross-dock of Goodwill, or the uncertainty in H-E-B data.Item Robust optimization and machine learning tools for adaptive transmission in wireless networks(2011-12) Yun, Sung-Ho; Caramanis, Constantine; de Veciana, Gustavo; Ghosh, Joydeep; Heath, Robert W.; Qiu, LiliCurrent and emerging wireless systems require adaptive transmissions to improve their throughput, to meet the QoS requirements or to maintain robust performance. However finding the optimal transmit parameters is getting more difficult due to the growing number of wireless devices that share the wireless medium and the increasing dimensions of transmit parameters, e.g., frequency, time and spatial domain. The performance of adaptive transmission policies derived from given measurements degrade when the environment changes. The policies need to either build up protection against those changes or tune themselves accordingly. Also, an adaptation for systems that take advantages of transmit diversity with finer granularity of resource allocation is hard to come up with due to the prohibitively large number of explicit and implicit environmental variables to take into account. The solutions to the simplified problems often fail due to incorrect assumptions and approximations. In this dissertation, we suggest two tools for adaptive transmission in changing complex environments. We show that adjustable robust optimization builds up protection upon the adaptive resource allocation in interference limited cellular broadband systems, yet maintains the flexibility to tune it according to temporally changing demand. Another tool we propose is based on a data driven approach called Support Vectors. We develop adaptive transmission policies to select the right set of transmit parameters in MIMO-OFDM wireless systems. While we don't explicitly consider all the related parameters, learning based algorithms implicitly take them all into account and result in the adaptation policies that fit optimally to the given environment. We extend the result to multicast traffic and show that the distributed algorithm combined with a data driven approach increases the system performance while keeping the required overhead for information exchange bounded.Item Robust optimization using NURBs based metamodels(2007-08) Ajetunmobi, Abiola Moruf; Crawford, Richard H.The subject of uncertainty is a prevalent factor in engineering and design. Real-world engineering systems are susceptible to uncontrollable dynamics or variations that influence their real-time performance and long-term consistency or reliability. Therefore designers and engineers desire to deliver system solutions that are both optimal and dependable. Robust design, in particular robust optimization has emerged as a promising methodology to address the problems of dealing with system uncertainty. The goal of robust optimization is to arrive at the optimized system configuration for a design objective (performance/objective function) that is tolerant to uncertain system variables through a strategy of minimizing the sensitivity of the system’s performance to the uncertain variables. The robust optimization approach creates representations of system perturbations/randomness, and develops measures of randomness and the designer’s risk aversion tolerance which are incorporated into identifying a robust optimal solution. This thesis presents a method for robust optimization that identifies robust regions and eliminates non-robust regions based on evaluations that estimate the gradients of the performance space topology across subspaces of NURBs based metamodel representations of a system’s design space. The thesis advances a new approach towards exploiting design space by searching for sections that could potentially hold robust solutions through analysis of the gradients across proximate clusters of control points in the control point networks inherent in NURBs metamodels and selectively optimizing only within the section(s) with the desired sensitivity profile to uncover robust optimal solutions. The HyPerROB algorithm is implemented in C++ and tested to prove the validity of its results in comparison to alternative methods in literature. This robust optimization framework is applied to formulate unconstrained robust optimization problems from three test functions and a constrained robust optimization problem from a practical engineering design problem.